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<TITLE>Computerized Diagnosis Press Release</TITLE>
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<H1>BREAST CANCER DIAGNOSIS1S VIA IMAGE ANALYSIS AND MACHINE LEARNING</H1>

<H2>W.H. Wolberg, W.N. Street, and O.L. Mangasarian</H2>

<P>
During the past six years, my colleagues and I at the
University of Wisconsin, Madison have taken a mathematical
technique originally developed for oil prospecting and modified it
to create a computerized method of diagnosing cancer in breast
tissue samples obtained by a technique called fine-needle
aspiration (FNA). In the past, interpreting FNA samples has been
rather subjective, but with our program we have been able to
develop an accurate and objective system of FNA interpretation.

<P>
With this computer program, we are also able for the first
time to calculate a mathematical probability that a sample is
malignant, rather than having to use fuzzy terms such as atypical
or suspicious. By sharing this probability data with patients, we
involve them in the decision-making process to a greater degree
than has previously been possible. In particular, instead of our
having to make a recommendation based solely upon our own values,
we can provide patients with the probability data and let them make
up their own minds as to subsequent treatment.

<P>
While we don't expect that our computer program will be
putting pathologists out of business any time soon, we do expect
that it will prove useful as an objective backup for pathologists
and assist them in improving their FNA interpretative skills.

<H2>THE ROLE OF FNA IN BREAST CANCER DIAGNOSIS</H2>


In the majority of all beast cancer cases, discovery of a mass
-- feeling a lump in the breast -- represents the first sign that
cancer is present. However, finding a breast mass does not
necessarily signal that cancer is present: indeed, most breast
masses are benign, not malignant.

<P>
Currently, the only definitive way to distinguish between
benign and malignant breast masses is for a pathologist to examine
a sample of breast tissue under a microscope. To obtain such
tissue, surgeons have traditionally performed a breast biopsy, in
which they take a patient to the operating room, administer
anesthesia, cut open the breast, and remove a piece of tissue.

<P>
More recently, FNA has emerged as a less invasive, less
painful, and less expensive alternative to surgical breast biopsy.
As its name suggests, breast FNA involves inserting a small needle
into the breast and suctioning cells into a syringe. The procedure
takes only minutes and does not require anesthesia.

<H2>THE PROBLEM OF FNA SUBJECTIVITY</H2>

Until recently, however, interpretation of an FNA specimen has
been a highly subjective process that, in the absence of firm
objective diagnostic criteria, relies heavily on the training and
experience of the persons examining the tissue. Although FNA has
gained wide acceptance for the diagnosis of cancer in the thyroid
and certain other organs, clinicians treating breast cancer have
usually preferred to employ less subjective diagnostic procedures
such as breast biopsy.

<P>
When looking under a microscope at pieces of breast biopsy
tissue, pathologists can examine the intact structure of breast
tissue and look for certain signs that provide definitive signs of
malignancy. One such sign, for instance, is call invasiveness: if
the pathologist sees a group of cells invading into normal tissue,
that invasion by itself is a sufficient indicator that cancer is
present.

<P>
The FNA technique, though, destroys the structure of breast
tissues and thus forces pathologists to base their interpretation
almost entirely on the appearance of the individual cells in the
sample. Pathologists examining a breast FNA specimen need to
evaluate a number of cell features, such as size, shape, and
various nuclear characteristics. However, since no single one of
these features, by itself, is able to yield an unequivocal
diagnosis of malignancy, pathologists have had to subjectively
weigh these various features in order to arrive at a diagnosis.

<P>
In instances when the various cell features point in the same
direction, the diagnosis of malignant vs. benign is clear-cut. In
other instances, though, when features point in different
directions -- some suggesting malignant and others suggesting
benign -- different observers might arrive at different verdicts,
depending on how they choose to weigh the conflicting pieces of
evidence. In the face of such potential for uncertainty and the
consequences of a wrong diagnosis, clinicians have been reluctant
to rely on FNA for diagnosing breast cancer.

<H2>PROSPECTING FOR CANCER</H2>


In 1987, during a chance encounter with Olvi L. Mangasarian,
Ph.D., a professor in the Computer Sciences department at UW-Madison,
I described to him the frustrations that I had encountered
in trying to find a more  objective way of interpreting FNA
specimens. He realized that my problem was analogous to a linear
programming problem he had studied some 20 years earlier while
working in the oil industry -- the problem of where to drill for
oil. Like evaluating FNA samples for cancer, deciding where to
drill for oil involves finding a way to weigh a number of factors,
such as geological features, no single one of which can
definitively predict whether or not one will find oil.

<P>
To solve his oil prospecting problem, Dr. Mangasarian
developed a method call multisurface pattern separation, which we
have now adapted for use with FNA samples. In essence, the method
begins with us mathematically modelling the cytological features,
such as size or shape, that we need to evaluate in order to
determine whether or not an FNA sample is malignant. Next we
"train" the computer with data from two sets of FNA samples for
which we already know the diagnosis -- one set of benign samples,
and a second set of malignant samples. During this machine
process, we iteratively build portions of a "fence" between the two
data sets until we have completely fenced off the two data sets
from each other.

<P>
Once we have trained the computer, we can then enter data from
an unknown FNA sample. The computer determines which side of the
"fence" the sample falls on and makes the appropriate diagnosis of
malignant or benign. The computer also calculates a probability of
malignancy which serves as a quantitative measure of the degree to
which the computer is certain of its diagnosis.

<H2>IMAGE ANALYSIS</H2>


Using digital image analysis techniques developed by Mr. Nick
Street, a Computer Science graduate student, we have now largely
automated our computerized system for interpreting FNA samples. We
look under a microscope to find suitable views of the sample and
use a video camera attached to the microscope to record the images.
The computer then digitizes the video pictures and stores the data
in computer files.

<P>
To run an FNA analysis, the computer program reconstructs the
digitized microscopic image and displays it on the computer screen.
The computer operator simply uses a mouse to trace out rough
outlines of the nuclei, and the computer does the rest. If
desired, the operator can trace out more nuclei or remove nuclei
that have already been traced out, and then return the analysis.

<H2>FINDINGS AND RESULTS</H2>


Our first goal has been to make the diagnosis of breast cancer
from FNA samples more objective and accurate than has previously
been possible. We have now succeeded in developing a highly
accurate diagnostic system that requires only a modest level of
expertise to operate. We now have a training set of 569 FNA
samples and calculate that the system will correctly diagnose
breast FNA samples 97% of the time. In practice, the system has
correctly diagnosed the last 92 samples that we have tested.

<P>
Our second goal has been to develop an objective method of
determining prognosis for those patients whom our diagnostic
program identifies as having breast cancer, that is, to accurately
predict the likelihood that their cancer will recur. In regards to
this goal, we are still at a relatively early stage of development,
as we only have 187 samples in our training set. Although we are
encouraged by our results to date, we still need to add more
samples to our training set and to improve the program's accuracy.

<P>
One of the key findings from our studies has been the
discovery that with our computer program, nuclear features (i.e.
nuclear "grade") are more accurate in determining prognosis than
are determinations of prognosis based on the traditional measures
of tumor size and axillary node status. Our research may thus have
a significant impact on the current practice of surgeon performing
axillary lymph node dissection at the time of mastectomy for
prognostic purposes.

<P>
Presently, determination of whether breast cancer has spread
into a patient's axillary lymph nodes is considered to be an
important factor in establishing prognosis: patients with tumor
spread in axillary lymph nodes (e.g. node positive) have a higher
likelihood of tumor recurrence than those without tumor spread into
axillary lymph nodes (e.g. node negative). Patients with node
positive tumors tend also to have earlier recurrences and, in
general, do worse than patients with node negative tumors.

<P>
Data from our research, however, have indicated that a
patient's nodal status (node positive vs. node negative) provides
no new additional information about prognosis beyond that which is
already available from an FNA biopsy. When we use FNA data alone
to run our prognostic program, the results are just as accurate as
when we run the program using FNA data plus information on nodal
status and tumor size. That is, we have found that analysis of a
patient's FNA sample alone provides all the information about
prognosis that we need, and thus that performing an axillary lymph
node dissection solely for prognostic purposes is unnecessary.
Should studies at other institutions confirm to our findings, many
breast cancer patients in the future could be spared the necessity
of undergoing axillary lymph node biopsy.

<H2>FUTURE DIRECTIONS</H2>


We are now seeking to collaborate with other institutions to
validate our computer program using their FNA samples. We have not
patented our program and are willing to provide copies of our
program to those researchers who sign collaborative agreements with
us. We recently analyzed 19 breast FNA slides from UCLA and were
gratified to find that we correctly diagnosed them all.

<P>
We are are also interested in expanding our computerized
interpretative method to other organs besides breasts. We have had
good early results with thyroid cancer and also believe our
approach could prove particularly useful for evaluating lymphomas.


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Last modified: Thu Jan  4 16:45:23 1996 by Nick Street
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